

##Point Estimate
correl=c(0.012,-0.039)
##Upper Bound on 95% C.I.
correl.ub=c(0.163,0.030)
##Calculating the Standard Error
correl.se=c((correl.ub[1]-correl[1])/1.96,(correl.ub[2]-correl[2])/1.96)
variable = c("Balanced","Unbalanced")
y.axis <- 1:length(variable)
par(oma=c(2,6,0,0))
plot(correl, y.axis, type = "n", axes = F, xlab = "", ylab = "", cex = 1.75, ylim = c(.75,2.25), xlim=c(-.25,.25),xaxs = "r", main = "")
segments(correl-1.96*correl.se, y.axis, correl+1.96*correl.se, y.axis,lwd =  1)
segments(correl-1.64*correl.se, y.axis, correl+1.64*correl.se, y.axis,lwd =  3)
points(correl,y.axis,pch=21,cex=1.75,bg = "black")
axis(2, at = y.axis, label = variable, las = 1, tick = T, cex.axis =1.5)#add y-axis and labels; las = 1 makes labels perpendicular to y-axis
axis(1,at = seq(-.25,.25, by = .05), label = seq(-.25,.25, by = .05), mgp = c(.8,2,1), cex.axis = 1.5)#add x-axis and labels; "pretty" creates a sequence of  equally spaced nice values that cover the range of the values in 'x'-- in this case, integers
abline(v=0,lty=2)
mtext("Professionalism/Economic Conditions Correlation",side=1,line=4,cex=2)


